The invention relates to a method for preparing NON-VIS (non-visible) images, and to a corresponding camera and a corresponding measuring arrangement.
Thermal imaging cameras are known as an example of such cameras for recording in a non-visible spectral range.
In the case of the known thermal imaging cameras, it is often desirable to improve the resolution that can be obtained by the detector unit present.
In principle, an option for improving the obtainable resolution consists of increasing the number of pixels in the detector apparatus. However, this is complicated from a design point of view and undesirably increases the production costs of the thermal imaging camera.
Furthermore, UV (ultraviolet) cameras, THz (terahertz) cameras, microwave cameras and other cameras for recording NON-VIS images in a non-visible spectral range are also known and these have comparable problems.
WO 2009/126445 A1 discloses a method for improving short-wave infrared images which uses SR (superresolution) and local processing techniques, wherein an image with a relatively high resolution is produced by nearest-neighbor interpolation, bilinear interpolation or bicubic interpolation.
MOHAMMAD S ALAM ET AL: “Infrared Image Registration and High-Resolution Reconstruction Using Multiple Translationally Shifted Aliased Video Frames” IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 49, no. 5, Oct. 1, 2000 (2000-10-01) discloses a method for infrared image registration and high-resolution reconstruction using multiple translationally shifted and aliased video frames, in which a gradient-based registration algorithm is used to obtain an estimate of the shifts between the captured frames and a weighted nearest-neighbor approach is used to place the frames into a uniform grid to produce a high-resolution image.
MIN KYU PARK ET AL: “Super-resolution image reconstruction: a technical overview”, IEEE SIGNAL PROCESSING MAGAZINE, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 20, no. 3, May 1, 2003 (2003-05-01), pages 21-36 discloses a technical overview of superresolution image reconstructions.
EUNCHEOL CHOI ET AL: “Super-resolution approach to overcome physical limitations of imaging sensors: an overview” INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, WILEY AND SONS, NEW YORK, US, vol. 14, no. 2, Jan. 1, 2004 (2004-01-01), pages 36-46 discloses an overview of the superresolution approach to overcome physical limitations of imaging sensors.
BAKER S ET AL: “Limits on super-resolution and how to break them”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, vol. 24, no. 9, Sep. 1, 2002 (2002-09-01), pages 1167-1183 discloses using Gaussian functions as point spread functions in superresolution reconstruction.
FILIP SROUBEK ET AL: “A Unified Approach to Superresolution and Multichannel Blind Deconvolution”, IEEE TRANSACTIONS ON IMAGE PROCESSING, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 16, no. 9, Sep. 1, 2007 (2007-09-01), pages 2322-2332 discloses a unified approach to superresolution and multichannel blind deconvolution, in which a point spread function characterizing the imaging process is computed recursively in an optimization method.
Steven W. Smith: “The Scientist and Engineer's Guide to Digital Signal Processing, Chapter 24: Linear Image Processing”, Jan. 1, 1997 (1997-01-01), pages 397-422 discloses linear image processing methods for processing images in the visible spectral range, in which two techniques are described for reducing the time required for implementation: convolution by separability and FFT convolution.
ZOMET A ET AL: “Robust super-resolution”, PROCEEDINGS 2001 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. CVPR 2001. KAUAI, HAWAII, DEC. 8-14, 2001; [PROCEEDINGS OF THE IEEE COMPUTER CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION], LOS ALAMITOS, CA, IEEE COMP. SOC, US, vol. 1, Dec. 8, 2001 (2001-12-08), pages 645-650 discloses a method for robust superresolution, in which a robust median estimator is combined in an iterative process to achieve a superresolution algorithm, wherein the process can increase the image resolution even in regions with outliers, where other superresolution methods actually degrade the image.
RUSSELL C HARDIE ET AL: “Joint MAP Registration and High-Resolution Image Estimation Using a Sequence of Undersampled Images” IEEE TRANSACTIONS ON IMAGE PROCESSING, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 6, no. 12, Dec. 1, 1997 (1997-12-01) discloses a method for the maximum a posteriori registration and high-resolution image estimation, in which a sequence of undersampled images is used.
ELAD M ET AL: “Fast and Robust Multiframe Super Resolution”, IEEE TRANSACTIONS ON IMAGE PROCESSING, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 13, no. 10, Oct. 1, 2004 (2004-10-01), pages 1327-1344 discloses a method for fast and robust multiframe superresolution, in which a high-resolution, experimentally produced image is shifted prior to convolution with a point spread function.
Michael E. Tipping ET AL: “Bayesian Image Super-Resolution”, Advances in Neural Information Processing Systems, Jan. 1, 2002 (2002-01-01), pages 1303-1310 discloses a method for Bayesian image superresolution, in which an unknown point spread function is estimated.